Automated bug fixing powered by LLMs

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Hey everyone, My friend and I built a simple bug fixing app that listens for alerts/issues from Sentry, contextualizes it against your codebase, and any other data sources you wish to connect (right now we support Notion, Google Docs, and Slack), and deploys an ai agent to write a PR for review in Github or Gitlab to solve the bug. Our current demo shows the end-to-end process for a trivial bug fix, but we have been testing it with open source python repos like http-pie, comparing how our agent solves a bug compared to a human engineer and it gets fairly close. We are working on adding unit tests for any bug we fix to make sure the code is actually merge worthy. The goal is to remove monotonous/low-level ticket work for SREs/Devs at startups/smaller companies pushing fast, eventually scaling to help gather context and solve more complex bugs. Lots of ideas of where to go from here! Let me know if you have feedback and you find this useful :) If you want access/use sentry I'm happy to onboard the first 10-15 people that want access today! Want to start slow bc I'm not sure what scale it can handle just yet. Just fill out the 'Get Started' form and I'll email you access, so far two of our friends companies are using it and like it. Thanks!

Visit product View on Hacker NewsNov 16, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, slack, google · Missing: mac, agents, macos

99%99% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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